• The logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, etc. When a cutoff probability is specified, the model acts as a binary classifier. Can also be extended into a multi-class classifier.

Is a:

• method

Applications:

• Classification
• Image Classification

Advantages:

• interpretable
• very simple
• widely used

Disadvantages:

• can suffer from Complete Separation - when a feature perfectly separates two classes, the model cannot be trained. In this case, a simple rule would work better anyway. - from Interpretable Machine Learning by Christoph Molnar
• interactions must be added manually
• other models may have better predictive performance